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Data Science Innovation Embraces Machine Learning at Seeq

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Seeq Corporation, a leading provider of advanced analytics software for manufacturing and Industrial Internet of Things (IIoT), announced the expansion of its machine learning capabilities into its applications. As a result of these improvements, organizations can utilize open-source, third-party, and data science investments to make them easier to access by employees on the front line.

Brian Parsonnet, CTO at Seeq Corporation, said, “Seeq provides a bridge between data science teams and their algorithms to front-line employees in hundreds of plants around the world. Deploying algorithms is now as simple as registering them in Seeq, and then defining which employees have access to each algorithm in their Seeq applications.”

By providing users with access to a variety of machine learning algorithms, Seeq is enabling machine learning innovation without forcing users to rely on a single vendor or platform. This allows organizations to utilize a variety of different algorithms, including the following:

  • Public resources and open-source algorithms. In this week’s release, Seeq will publish two Seeq Add-ons to GitHub, which will include algorithms and workflows for correlation and clustering analytics that users can modify and enhance as desired.
  • Algorithms developed by customers in Seeq Data Lab, or machine learning operations platforms like Microsoft Azure Machine Learning, Amazon SageMaker, Anaconda, and others in support of data science and digital transformation initiatives.
  • Algorithms developed by third-party software vendors, academic institutions, and partners. There is an emerging marketplace for algorithms that are tailored to business and industry-specific needs, including AWS Lookout for Equipment, Microsoft Azure AutoML, and Brigham Young University’s open-source offerings.

Kevin Prouty, VP Industrials, IDC Corporation, said, “Data science innovation in manufacturing organizations has the potential to deliver a step-change in plant sustainability, productivity, and availability metrics. But to land this opportunity, companies must be able to deploy data science innovation to frontline engineers with the expertise, data, and plant context to make decisions on insights provided by these new algorithms.”

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